1. Field of the Invention
The present invention relates a method and apparatus to improve medical imaging by standardizing image quality analysis, which directly links the quality data with the imaging dataset (i.e. image-centric), continuously tracks and records image quality data for longitudinal analysis, and analyzes all imaging datasets while taking into account a myriad of clinical, technical, and patient-specific variables affecting quality measures.
2. Description of the Related Art
In current medical practice, assessment of quality is highly subjective and personalized, personifying the old adage “beauty (i.e., quality) is in the eyes of the beholder”. While almost all healthcare participants view quality as a high priority, standardized methods for quantitative and qualitative quality assessment are essentially non-existent. This lack of standardization in medical quality assessment produces an environment where quality deliverables are often left to the discretion of individual healthcare providers, who at the same time are being challenged to maintain revenue and profitability in an environment of declining economic reimbursements.
The net result is that quality often becomes sacrificed (or subservient) to concerns over productivity and workflow. In the absence of standardized quality metrics and tools for analysis, quality deliverables in healthcare will continue to stagnate (and potentially deteriorate), with the potential for adverse clinical outcomes and economic inefficiency.
Further, while all medical disciplines are affected to some degree by the relative lack of objective standards and methods of analysis, medical imaging is perhaps more prone to deficiencies and variation in quality due to its dependence on imaging (i.e., pictorial) data, as opposed to clinical (i.e., numerical) data. What one imaging provider (e.g., technologist, radiologist, administrator) may view as a “high quality” imaging dataset, another provider may view the same imaging dataset as possessing marginal or even poor quality. In the absence of an objective methodology to analyze image quality, the net result is that quality assessment in everyday practice remains somewhat elusive and undefined. This creates an environment where quality assessment is no longer focused on the imaging dataset (and the inherent diagnostic information derived from it), but instead on operational efficiency metrics (e.g., scheduling, report turnaround, and patient waiting times) which is easily measured and defined in numerical terms. As a result, many consumers of medical imaging services (e.g., referring clinicians, patients, third party payers) currently evaluate quality in these easily defined operational efficiency measures and largely ignore the intrinsic “clinical” quality of the imaging dataset, which ultimately determines the ability to render an accurate and reproducible diagnosis.
In medical imaging, image quality assessment can be performed internally or externally. Internal image quality assessment is typically performed by the technologist performing image acquisition, which usually includes a cursory review of the imaging dataset upon completion. This process is almost always “off the record” and results in no recorded data for longitudinal analysis. If an image quality deficiency is identified, the technologist makes a decision to allow the imaging dataset to be accepted “as-is”, or elects to repeat the exam in part or in total, depending upon the exam type and severity of the deficiency. Unfortunately, in the current practice environment, productivity often tends to supersede quality, and as a result many imaging exams lacking in quality are accepted “as-is”.
While the interpreting radiologist in theory serves as a “second line of defense” in maintaining quality standards, there is little incentive for the radiologist to override the decision of the technologist once the exam has been completed, the patient has left the department, and the imaging dataset has been transmitted to the imaging archive for interpretation. In the event that the radiologist perceived a significant quality deficiency warranting repeating of the imaging exam prior to interpretation, the patient would have to be recalled and the exam repeated. This has the undesired effect of increased radiation, reducing patient throughout, creating a backlog in the queue, and delaying diagnosis. As a result of these negative pressures, many imaging service providers tend to “make do” and accept image quality deficiencies, which in turn has the potential to result in equivocal or erroneous diagnoses, additional follow-up imaging exams, and additional consultations. Even in those relatively rare situations where a quality deficiency has resulted in an intervention (i.e., repeat exam or additional images), there is rarely any documentation recorded as to the specific nature and severity of the quality deficiency or involved portions of the imaging dataset. This results in incomplete imaging records, lack of traceable quality data, and lost educational opportunities.
External image quality assessment typically takes place when an imaging service provider is going through an accreditation process, which is customarily required for reimbursement by third parties. In this scenario, the medical imaging provider is typically asked to provide their operating procedures for review, along with a number of representative medical images from the modalities being evaluated for accreditation. Since the goal is aimed at passing the review process with the minimal amount of effort and scrutiny, the provider seeks out images of the highest quality. In doing so however, the broad spectrum of images is never really evaluated and this can mask existing quality deficiencies. The educational opportunity for critical review is replaced by a static and binary process of pass/fail. Once the accreditation process has been successfully completed, the provider often returns to “business as usual”, repeating the same mistakes and quality imperfections as before the accreditation/review process took place. Since this accreditation/review process only takes place every 3-4 years, there is little incentive to critically review quality deliverables on a regular basis. The end result is that quality assessment in its current form is inherently flawed, performed in a piecemeal fashion, devoid of data, and lacks documentation.
While the current practice environment in quality assessment is flawed, it does create an opportunity for tremendous improvement. The key is to evaluate the existing practice deficiencies and create strategies and technologies which counteract and improve upon existing loopholes and gaps in medical imaging assessment.
Thus, a desired solution lies in creating objective and reproducible quality standards which can analyze the various steps and processes, players, and technologies in the healthcare continuum, with the goal of using this data to determine best practice guidelines and provide economic incentives to reward high-quality providers.